Research Scientist / Engineer – Reinforcement Learning Infrastructure
SF Bay Area, CA, Remote, US, Remote, International, London, UK
R
USD 200K-300K (estimate) Senior-level Full Time
Tasks
- Build high throughput rollout generation
- Build reward model serving pipelines
- Collaborate with researchers to operationalize RL training ideas
- Create LLM as judge evaluation pipelines
- Design curriculum and task sampling
- Design distributed reinforcement learning post training systems
- Develop evaluation monitoring and debugging tooling
- Develop reward infrastructure and verifiable rewards
- Implement defenses against reward hacking
- Implement reinforcement learning environments for multi step tasks
- Implement sequence packing for long trajectories
- Improve training efficiency and stability
- Integrate inference engines into training loop
- Reuse KV cache across rollouts
- Schedule heterogeneous training and inference workloads
Perks/Benefits
- N/A
Skills/Tech-stack
Asynchronous training | Containerization | Curriculum learning | Distributed Systems | Distributed Training | FSDP | GPU Cluster | Human Feedback | KV cache | Kubernetes | LLM Evaluation | Language Models | Large Language Models | Learning from Human Feedback | MPI | Mixture of Experts | Model Synchronization | NCCL | Off Policy | Off Policy Learning | Pipeline parallelism | Policy Optimization | Policy learning | Proximal Policy Optimization | PyTorch | Ray | Reinforcement Learning | Reinforcement Learning from Human Feedback | Reward Hacking | Reward Modeling | SGLang | Sandboxing | Sequence Packing | Tensor Parallelism | Tool use | VLLM
Education
N/A
Regions
Countries
States
Cities
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